Socioeconomic position is a key determinant of individual mental health, including depression and anxiety disorders. Depression and anxiety disorders inflict major social and economic burden on families and communities. Recent studies suggest that the community socioeconomic context, in particular its degree of income inequality, contribute to a person's morbidity and mortality, even after individual socioeconomic position has been taken into account; a disturbing finding as income inequality has grown in the US during the last three decades. The proposed analyses will expand this research program on socioeconomic context to include psychiatric disorders. We will examine the relationship between socioeconomic residential context and the prevalence of major depression and anxiety disorders in Baltimore neighborhoods using the Epidemiologic Catchment Area- Follow Up survey sample, and in US counties, using the National Comorbidity Survey sample. In addition to income inequality, we will examine the relationship between alternative contextual socioeconomic indicators at the county and neighborhood levels (i.e., absolute income, poverty and social capital) and major depression and anxiety disorders. This will be the first population-based analysis to examine the association between residential socioeconomic context in relation to the prevalence of depression and anxiety disorders in the US. Our analysis will also make important methodological contributions to the field of psychiatric epidemiology. Socioeconomic context will be measured at the individual, neighborhood (FCA-F) and county (NCS) levels, and we will apply innovative statistical methods that are appropriate for the simultaneous analysis of two levels of data and interactions between contextual and individual-level variables. Testing hypothetical mechanisms that may relate contextual and individual socioeconomic attributes (e.g., age, gender, race/ethnicity, socioeconomic position) will be an area of concentration of this analysis, as it has been overlooked in most multilevel studies to date. The results of these analyses will have implications for local health policy in urban neighborhoods and counties across the US that have the potential to reduce the social costs associated with depression and anxiety disorders.